A task-oriented dialog can be formulated as a decision-making process under the RL framework [1]}, [2]}. The system navigates in a Markov Decision Process (MDP), interacting with its environment (e.g., users and task-specific KBs) over a sequence of discrete steps. At each step \(t\) , the system observes the current dialog state \(s_t\) , chooses an action \(a\)  according to a policy \(\pi \) , and then receives a reward \(r_t\)  and observes a new state \(s_{t+1}\) , continuing the cycle until the episode terminates. The goal of RL is to find the optimal policy to maximize expected rewards.
